{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c3abe0a1-b54e-48aa-b681-6c44c1e0c7b0",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.decomposition import PCA\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "378128b0-ea00-49f9-958a-4b04811e05df",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "iris= load_iris()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e0948336-f1c1-478b-b305-c73f0d1ffd84",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X = iris.data\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4abc42b2-f49e-4485-a183-7597f347b224",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((150, 4), (150,))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape,y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "51d74267-92c7-4e8d-b32c-030504d6809a",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2,svd_solver=\"full\").fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2df47372-a878-4125-ab89-05f51bd0c1d8",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 2)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "20b680bd-a328-469e-adc2-4098b6804963",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "c1 = y == 0\n",
    "c2 = y == 1\n",
    "c3 = y == 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "371fb6ad-9e1b-4d33-bd11-ea0645b172f4",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d3e1f77e88>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pca[c1][:,0],pca[c1][:,1],c=\"r\")\n",
    "plt.scatter(pca[c2][:,0],pca[c1][:,1],c=\"b\")\n",
    "plt.scatter(pca[c3][:,0],pca[c1][:,1],c=\"g\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f83a1011-6751-40a4-a577-76138940ae26",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}